A novel structure adaptive discrete grey Riccati model and its application in energy production and consumption

Yong Wang, Shixiong Wen, Wenyu Kuang, Neng Fan, Zhongsen Yang, Mou Yang, Hong-Li Li, Flavian Emmanuel Sapnken and Govindasami Narayanan

Energy, 2025, vol. 333, issue C

Abstract: To address climate change, the international community has broadly supported clean energy. In recent years, energy structure has consistently transitioned toward low-carbon and environmentally friendly solutions, and it has now entered a critical phase. Therefore, accurately predicting future energy development trends is of significant importance for energy structure transformation. This paper proposes a novel structurally adaptive discrete grey Riccati model, called SADGRM(1,1), for energy data prediction. The Hausdorff fractional-order cumulative generation operation is utilized to implement the principle of prioritizing new information. By integrating a nonlinear dynamic structural component based on the grey Riccati model, the model can adapt its structure to the data characteristics, thereby enhancing its flexibility and adaptability. The differential evolution algorithm (DE) was chosen to optimize the model parameters after evaluating the performance of several algorithms. To address reliability issues arising from the optimization algorithm, Monte Carlo simulations and probability density analyses were conducted to validate the robustness of the model. The results indicate that the model is stable and reliable. Three actual cases of hydropower generation, urban natural gas supply and average daily natural gas consumption are predicted. The results indicate that the novel model is an effective instrument for energy forecasting and offers considerable reference value for decision-making in energy development.

Keywords: Grey model; Differential evolution algorithm; Monte-Carlo simulation; Energy production and consumption (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225030129
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:333:y:2025:i:c:s0360544225030129

DOI: 10.1016/j.energy.2025.137370

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().